The Spatiotemporal Distribution of Flash Floods and Analysis of Partition Driving Forces in Yunnan Province

Junnan Xiong,Weiming Cheng,Chongchong Ye,Liang Guo,Chenghu Zhou,Xiaolei Zhang

doi:10.3390/su11102926

Abstract

Flash floods are one of the most serious natural disasters, and have a significant impact on economic development. In this study, we employed the spatiotemporal analysis method to measure the spatial–temporal distribution of flash floods and examined the relationship between flash floods and driving factors in different subregions of landcover. Furthermore, we analyzed the response of flash floods on the economic development by sensitivity analysis. The results indicated that the number of flash floods occurring annually increased gradually from 1949 to 2015, and regions with a high quantity of flash floods were concentrated in Zhaotong, Qujing, Kunming, Yuxi, Chuxiong, Dali, and Baoshan. Specifically, precipitation and elevation had a more significant effect on flash floods in the settlement than in other subregions, with a high r (Pearson’s correlation coefficient) value of 0.675, 0.674, 0.593, 0.519, and 0.395 for the 10 min precipitation in 20-year return period, elevation, 60 min precipitation in 20-year return period, 24 h precipitation in 20-year return period, and 6 h precipitation in 20-year return period, respectively. The sensitivity analysis showed that the Kunming had the highest sensitivity (S = 21.86) during 2000–2005. Based on the research results, we should focus on heavy precipitation events for flash flood prevention and forecasting in the short term; but human activities and ecosystem vulnerability should be controlled over the long term.

Highlights

Flash floods are one of the most severe natural disasters to try to prevent and deal with in the aftermath
The objectives of this study were to (1) measure the spatial–temporal variation of flash floods using the changepoint, kernel density estimation, spatial mismatch analysis, standard deviational ellipse (SDE), and spatial gravity center model; (2) analyze the driving factors for the spatial pattern of flash floods in different subregions of the landcover using the Pearson correlation coefficient, multiple linear regression, and principal component analysis; and (3) conduct a sensitivity analysis to investigate the response of flash floods on economic development
On the basis of the descriptions in Section 2.3.1, annual flash floods were divided into four time periods, the number of flash floods was 104, 331, 704, and 2027 during 1949–1962, 1963–1981, 1982–1995, and 1996–2015, respectively (Figure 2a)

Summary

Introduction

Flash floods are one of the most severe natural disasters to try to prevent and deal with in the aftermath. They are responsible for loss of life and serious destruction to property and infrastructure, severely affecting a region’s economic development [1,2,3]. According to an investigation by the World Meteorological Organization, the loss of property resulting from flash floods ranks in the top 10 among a range of natural disasters in 75% of countries [4]. As an economically developed country, flash floods in the United States ranked first in causes of death, with approximately 100 lives lost each year [5]. Yunnan is one of the most important areas of ecological value in the world [9] where flash floods are rapidly increasing, causing serious threat to people’s lives and property [10]

Objectives

Methods

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Discussion

Conclusion